Methods and systems for analyzing samples

ABSTRACT

The disclosure features methods for analyzing a sample, the methods including exposing the sample to plurality of pulses of electromagnetic radiation to convert a portion of the sample into a plasma, recording a spectrum of electromagnetic radiation emitted in response to each of the plurality of pulses to define a sequence of spectra for the sample, and using an electronic processor to determine information about the sample based on the spectra, where exposing the sample to the plurality of pulses of electromagnetic radiation includes directing the pulses to be incident on different spatial regions of the sample, and where a temporal delay between exposing the sample to each successive radiation pulse is constant.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 61/790,874, filed on Mar. 15, 2013, the entire contents of which areincorporated herein by reference.

TECHNICAL FIELD

This disclosure relates to methods and systems for analyzing samples ofmaterials, and more particularly to methods and systems for determiningthe identity, place of origin, and/or treatment history of an unknownsample.

BACKGROUND

Provenance determination of materials (e.g., minerals) is useful for avariety of reasons. For example, materials from one location may be morevaluable than those from another location. In addition, laws restrictingthe sale of materials from certain areas may exist due to geopoliticalconcerns. Currently, distinguishing between particular conflict oredeposits (e.g., columbite and tantalite) requires a combination ofmineralogical, geochemical and geochronological analyses, which can beboth time consuming and catastrophically destructive to the sample. Inthe case of rare and highly valuable materials, non-destructiveanalytical tools are typically necessary to preserve the integrity ofthe sample.

As it currently stands, non-destructive origin determination is largelybased on a combination of human observations and data collected fromadvanced analytical instrumentation. Final determination decisionstypically fall to the uncertain and sometimes varying opinions ofresearch scientists. Techniques traditionally used for origindetermination include Raman and Luminescence Spectroscopy, X-rayRadiography, Tomography, Energy-Dispersive X-Ray Fluorescence (EDXRF),and Scanning Electron Microscope Energy-Dispersive Spectroscopy(SEM-EDS). Secondary Ion Mass Spectrometry (SIMS) and Laser-AblationInductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) have alsorecently been applied to provenance determination studies. Each of theabove techniques offers both advantages and disadvantages.

SUMMARY

This disclosure is based on the unexpected discovery that the entireemission spectrum of a sample of a physical material (e.g., a gemstone)can be used to determine its place of origin more accurately than aconventional method using only a portion of the emission spectrum of asample. In addition, this disclosure is based on the unexpecteddiscovery that variations in the emission spectrum of a physicalmaterial resulting from sample non-homogeneity and/or differentexcitation conditions should not be discarded as noise or averaged, butinstead can be used to more accurately determine the identity of thesample.

The methods described herein are based on an assumption that everymaterial, natural or man-made, bears traces of the materials andprocesses involved in its creation. Every sample of material, ifexamined in sufficient detail, is different from every other sample. Themethods described herein use those traces of formation to classifysamples according to their similarities and differences.

Provenance determination is useful for a variety of reasons, such asdetermining the value of the material (e.g., a mineral) or whether amaterial (e.g., a manufactured material) has been made to itsspecifications. “Provenance” mentioned herein can refer to geographicalsites of discovery in the case of natural materials such as minerals,but can also refer to a particular factory, process, or manufacturer inthe case of man-made materials. Provenance determination of man-madematerials allows for the identification of counterfeit products and/orsubstandard products.

In one aspect, this disclosure features a method for analyzing a sample.The method includes (a) converting a portion of the sample into a plasmamultiple times; (b) recording a spectrum of electromagnetic radiationemitted in response to each of the sample conversions to define asequence of spectra for the sample, in which each member of the sequencecorresponds to the spectrum recorded in response to a different one ofthe sample conversions; (c) using an electronic processor to compare thesequence of spectra for the sample to a sequence of spectra for each ofmultiple reference samples in a reference library; and (d) using theelectronic processor to determine information about the sample based onthe comparison to the multiple reference samples in the library.

In another aspect, this disclosure features a system for analyzing asample. The system includes (a) an excitation source for converting aportion of the sample into a plasma multiple times; (b) a spectrometerconfigured to record a spectrum of electromagnetic radiation in responseto each of the sample conversions to define a sequence of spectra forthe sample, in which each member of the sequence corresponds to thespectrum recorded in response to a different one of the sampleconversions; and (c) an electronic processor configured to compare thesequence of spectra for the sample to a sequence of spectra for each ofmultiple reference samples in a reference library and determineinformation about the sample based on the comparison to the multiplereference samples in the library.

Alternatively, in each of these aspects, there can be as a few as onereference sample in the reference library, in which case, the electronicprocessor can determine the information about the sample based on thecomparison to the sequence of spectra in the one reference sample in thelibrary. This is appropriate when the desired information about thesample being analyzed is, for example, a simple verification orauthentication that the sample being analyzed does correspond, or doesnot correspond, to this single reference sample.

Embodiments with respect to any of these four aspects can include one ormore of the following features.

In some embodiments, a pulse of electromagnetic radiation is used toconvert the sample into the plasma for each of the multiple times. Insome embodiments, the pulse of electromagnetic radiation can be derivedfrom a laser, an ion beam, an electron beam, or an arc discharge. Forexample, the pulse of electromagnetic radiation can be derived from alaser and causes laser-induced breakdown of the sample.

In some embodiments, the sample is a solid (e.g., a gemstone, a metal, amanufactured material, such as a manufactured metal alloy, or abiological material). In certain embodiments, the sample is a liquid(e.g., the sample is blood, urine, oil, or water).

In some embodiments, the one or more reference samples are metal alloyshaving a common elemental composition and different processingprotocols, such as different heat treatments.

In some embodiments, the sample being analyzed and the one or morereference samples can be metal alloys having a common elementalcomposition, and wherein the information determined by the electronicprocessor is whether the sample being analyzed has been subjected to aspecific processing protocol corresponding to one of the referencesamples.

In some embodiments, the conversion of the sample into the plasma causesthe sample to emit electromagnetic radiation indicative of atomicemissions. In certain embodiments, the conversion of the sample into theplasma further causes the sample to emit electromagnetic radiationindicative of one or more of isotopic emissions, molecular emissions,molecular isotopic emissions, and spectral interference between atomicemissions from different atoms in the sample.

In some embodiments, each spectrum is recorded with a spectralresolution sufficient to resolve the emission of electromagneticradiation corresponding to atomic emission and one or more of isotopicemission, molecular emission, molecular isotopic emission, and spectralinterference between atomic emissions from different atoms. For example,each spectrum can be measured with a spectral resolution containing atleast 10,000 channels. As another example, each spectrum can be measuredwith a spectral resolution finer than 0.1 nm, and preferably finer than0.06 nm.

In some embodiments, each spectrum is measured over a range includingfrom 195 nm to 1005 nm.

In some embodiments, members of the sequence for the sample correspondto the spectra recorded in response to different parameters for thepulse of electromagnetic radiation used to convert the portion of thesample into the plasma during the multiple times (e.g., multipleexcitations). For example, the different parameters can includedifferent pulse energies, different pulse durations, different pulsewavelengths, or combinations thereof.

In some embodiments, members of the sequence for the sample correspondto the spectra recorded in response to different incident locations onthe sample for the pulse of electromagnetic radiation used to convertthe portion of the sample into the plasma during the multiple times(e.g., multiple excitations). For example, the different incidentlocations can be sufficient to characterize heterogeneity in the atomiccomposition of the sample. In some embodiments, the different locationsare separated from one another by at least 10 μm. In some embodiments,the different incident locations include at least 10 different locations(e.g., at least 15 different locations or at least 64 differentlocations).

In some embodiments, members of the sequence for the sample correspondto the spectra recorded in response to combinations of differentparameters for the pulse of electromagnetic radiation used to convertthe portion of the sample into the plasma during the multiple times(e.g., multiple excitations) and different incident locations on thesample for the pulse of electromagnetic radiation used to convert theportion of the sample into the plasma during the multiple times.

In some embodiments, the sequence of spectra for the sample can includemembers corresponding to all of the different spectra recorded for thesample during the multiple times (e.g., multiple excitations). As usedherein, each “member” corresponds to a unique spectrum in the sequenceof spectra. The set of such members define the “constituent” spectra forthe sequence.

In some embodiments, the electronic processor can determine the membersof the sequence of spectra for the sample by using a cluster technique.Such analysis can be applied to the sequence of spectra for the samplebeing analyzed and/or to the sequence of spectra for any of thereference samples.

In some embodiments, the comparison by the electronic processorcomprises comparing a probability distribution for the members ofsequence of spectra in the sample being analyzed to a probabilitydistribution for the members of the sequence of spectra for each of thereference samples. For example, the probability distribution for thesample being analyzed can be represented as a histogram indicating thenumber of times each member occurs in the sequence of spectra for thesample being analyzed and the probability distribution for the membersof each reference sample can be represented as a histogram indicatingthe number of times each member occurs in the sequence of spectra foreach reference sample.

In some embodiments, the comparison by the electronic processor caninclude identifying a degree to which the sequence for the samplematches a sequence for each of at least some of the reference samples inthe library. For example, identifying a degree can include (a) comparingeach spectrum in the sequence for the sample to the different spectra inthe library to identify the different spectra from the library mostlikely to match the spectra in the sequence for the sample; (b)identifying which reference samples from the library comprise all of theidentified spectra; and (c) identifying a degree to which the sequencefor the sample matches a sequence for each of the identified referencesamples. In some embodiments, the electronic processor uses a nearestneighbor algorithm to perform one or both of the above identifyingsteps.

In another example, identifying the degree to which the sequence forsample matches a sequence for each of the reference samples comprisescomparing a probability distribution for the members of the sample beinganalyzed to a probability distribution for the members of the sequenceof spectra for each of the reference samples.

In some embodiments, the reference library is made by (a) providinginformation about the identity of each reference sample; (b) convertinga portion of each reference sample into a plasma multiple times; and (c)recording a spectrum of electromagnetic radiation emitted from eachreference sample in response to each of the reference sample conversionsto define a sequence of spectra for each reference sample, wherein eachmember of the reference sample sequence corresponds to the spectrumrecorded in response to a different one of the reference sampleconversions. For example, members of each reference sample sequencecorrespond to the spectra recorded in response to combinations ofdifferent parameters for a pulse of electromagnetic radiation used toconvert the portion of each reference sample into the plasma during themultiple times (e.g., multiple excitations) and different incidentlocations on each reference sample for the pulse of electromagneticradiation used to convert the reference sample into the plasma duringthe multiple times.

In some embodiments, the information about the sample can include anidentity or a provenance for the sample.

In a further aspect, the disclosure features methods for analyzing asample that include exposing the sample to plurality of pulses ofelectromagnetic radiation to convert a portion of the sample into aplasma, recording a spectrum of electromagnetic radiation emitted inresponse to each of the plurality of pulses to define a sequence ofspectra for the sample, where each member of the sequence corresponds tothe spectrum recorded in response to a different one of the pulses,using an electronic processor to compare the sequence of spectra for thesample to a sequence of spectra for each of multiple reference samplesin a reference library, and using the electronic processor to determineinformation about the sample based on the comparison to the multiplereference samples in the library, where exposing the sample to theplurality of pulses of electromagnetic radiation includes directing thepulses to be incident on different spatial regions of the sample, andwhere a temporal delay between exposing the sample to each successiveradiation pulse is constant.

Embodiments of the methods can include any one or more of the followingfeatures.

The plurality of pulses of electromagnetic radiation can form ahexagonal array on the sample. The plurality of pulses ofelectromagnetic radiation can form an array of equally spaced exposureregions on the sample. The exposure regions can be positioned along acircumference of a common circle. The plurality of pulses can include 60or more pulses.

The methods can include selecting the temporal delay between successiveradiation pulses by adjusting a spacing between the different spatialregions of the sample. The methods can include selecting the temporaldelay based on the sample. The methods can include selecting a power ofeach of the plurality of radiation pulses.

The methods can include measuring a first sequence of spectra for thesample corresponding to a first constant temporal delay betweensuccessive radiation pulses, and measuring a second sequence of spectrafor the sample corresponding to a second constant temporal delay betweensuccessive radiation pulses that is different from the first constanttemporal delay. The methods can include determining the informationabout the sample based on the first and second sequences of spectra.

Embodiments of the methods can also include any of the other features orsteps disclosed herein, including features and steps disclosed inconnection with different embodiments, in any combination asappropriate.

In another aspect, the disclosure features systems for analyzing asample, the systems including an electromagnetic radiation sourceconfigured to expose the sample to plurality of pulses ofelectromagnetic radiation to convert a portion of the sample into aplasma, a detector configured to record a spectrum of electromagneticradiation emitted in response to each of the plurality of pulses todefine a sequence of spectra for the sample, where each member of thesequence corresponds to the spectrum recorded in response to a differentone of the pulses, and an electronic processor configured to: controlthe radiation source so that the plurality of electromagnetic radiationpulses are incident on different spatial regions of the sample, where atemporal delay between exposing the sample to each successive radiationpulse is constant; compare the sequence of spectra for the sample to asequence of spectra for each of multiple reference samples in areference library; and determine information about the sample based onthe comparison to the multiple reference samples in the library.

Embodiments of the systems can include any one or more of the followingfeatures.

The electronic processor can be configured to control the radiationsource so that the plurality of pulses of electromagnetic radiation forma hexagonal array on the sample. The electronic processor can beconfigured to control the radiation source so that the plurality ofpulses of electromagnetic radiation form an array of equally spacedexposure regions on the sample. The exposure regions can be equallyspaced along a circumference of a circle. The plurality of pulses caninclude 60 or more pulses.

The electronic processor can be configured to select the temporal delaybetween successive radiation pulses by adjusting a spacing between thedifferent spatial regions of the sample. The electronic processor can beconfigured to select the temporal delay based on the sample. Theelectronic processor can be configured to control the radiation sourceto select a power of each of the plurality of radiation pulses.

The electronic processor is configured to use the detector to measure afirst sequence of spectra for the sample corresponding to a firstconstant temporal delay between successive radiation pulses, and use thedetector to measure a second sequence of spectra for the samplecorresponding to a second constant temporal delay between successiveradiation pulses that is different from the first constant temporaldelay. The electronic processor can be configured to determine theinformation about the sample based on the first and second sequences ofspectra.

Embodiments of the systems can also include any of the other featuresdisclosed herein, including features disclosed in connection withdifferent embodiments, in any combination as appropriate.

Certain features, aspects, and steps are disclosed herein in connectionwith particular embodiments. In general, however, those features,aspects, and steps are not particular to those embodiments, and can becombined with other embodiments and other features, aspects, and stepsas desired. Accordingly, while particular embodiments have beendescribed herein for purposes of illustration, it should be appreciatedthat other combinations of the features, aspects, and steps disclosedherein are also within the scope of the disclosure, and that particularembodiments described herein can also include features, aspects, andsteps disclosed in connection with other embodiments.

The entire contents of each of the following are incorporated byreference herein: U.S. Provisional Application No. 61/595,903, filed onFeb. 7, 2012; U.S. patent application Ser. No. 13/760,349, filed on Feb.6, 2013; and PCT Patent Application No. PCT/US2013/024843, filed on Feb.6, 2013.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this disclosure belongs. Although methods and materialssimilar or equivalent to those described herein can be used in thepractice or testing of the subject matter herein, suitable methods andmaterials are described below. All publications, patent applications,patents, and other references mentioned herein are incorporated byreference in their entirety. In case of conflict, the presentspecification, including definitions, will control. In addition, thematerials, methods, and examples are illustrative only and not intendedto be limiting.

Other features and advantages of the disclosure will be apparent fromthe description, drawings, and claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a flow chart showing a series of exemplary steps for comparinga sample to the reference samples in a reference library to obtaininformation about the sample.

FIG. 2 is a flow chart showing a series of exemplary steps of performingstep (c) in FIG. 1.

FIG. 3 is a flow chart showing another series of exemplary steps ofperforming step (c) in FIG. 1

FIG. 4 is an illustration of an exemplary system for acquiring andanalyzing an emission spectrum of a sample.

FIG. 5 shows three graphs showing constituent spectra for sample of 17-4Stainless Steel subjected to a specific processing condition.

FIG. 6 is a schematic diagram of an analysis system.

FIG. 7 is a schematic diagram of a fiber optic radiation collector.

FIG. 8 is a graph showing scattered light intensity as a function ofsample position.

FIG. 9 is a schematic diagram of a hexagonal exposure spot pattern.

FIG. 10 is a schematic diagram of a radial exposure spot pattern.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

In general, this disclosure relates to methods and systems for analyzinga sample (e.g., to determine the identity and/or place of origin of thesample) by using the entire emission spectrum of the sample. In someembodiments, determining the identity of a sample can includedetermining whether a sample is manufactured according to a certainspecification.

In some embodiments, the methods disclosed herein include acquiring oneor more emission spectra of an unknown sample (i.e., a sample with anunknown place of origin or an unknown identity) and comparing theacquired spectra with the spectra of each reference sample (i.e., asample with a known place of origin or a known identity) in a referencelibrary. The place of origin or the identity of the unknown sample canthen be determined when its spectra match (i.e., has sufficientsimilarity to) those of one or more reference samples in the library. Insome embodiments, the library can include only one reference sample. Insuch embodiments, the methods disclosed herein can be used to verifywhether the unknown sample is the same as, or different from, thereference sample (e.g., for verification or authentication).)

In general, the samples that can be analyzed by the methods disclosedherein can include any suitable materials, such as a geological material(e.g., minerals, gemstones, rocks, meteorites, or metals), amanufactured material (e.g., machined metal parts, cast metal parts, orpharmaceuticals), or a biological material (e.g., pathogens, bacteria,viruses, foods, or woods). Exemplary minerals include beryl, corundum,tourmaline, diamond, gold, wolframite, cassiterite, and columbite andtantalite (COLTAN). Exemplary gemstones include diamonds, emeralds,rubies, and sapphires. Exemplary rocks include limestones, marbles, andgranites. In some embodiments, the samples that can be analyzed by themethods disclosed herein can be an inorganic material (e.g., gemstones)or an organic material (e.g., apples or oranges). In some embodiments,the samples that can be analyzed by the methods disclosed herein aresolid samples or liquid samples (e.g., blood, urine, oil, or water).

In one application, the sample being analyzed and the one or morereference samples can be manufactured parts (e.g., metal alloys) thatare subject to different processing conditions (e.g., different heattreatments). In such cases, the elemental compositions of the parts maybe the same, but the different processing conditions cause the materialsto have different properties. The techniques disclosed herein candistinguish between such parts.

FIG. 1 is a flow chart showing a series of exemplary steps for comparinga sample with the reference samples in a reference library to obtaininformation about the sample. As shown in FIG. 1, the methods disclosedherein can include the following steps: (a) converting a portion of asample into a plasma multiple times; (b) recording a spectrum ofelectromagnetic radiation emitted in response to each of the sampleconversions to define a sequence of spectra for the sample, in whicheach member of the sequence corresponds to the spectrum recorded inresponse to a different one of the sample conversions; (c) using anelectronic processor to compare the sequence of spectra for the sampleto a sequence of spectra for each of multiple reference samples in areference library; and (d) using the electronic processor to determineinformation about the sample based on the comparison to the multiplereference samples in the library.

Step (a) can be performed by irradiating a pulse of electromagneticradiation to the sample. In general, the electromagnetic radiation hassufficient energy to convert a portion of the sample into a plasma.Exemplary electromagnetic radiations include a laser beam (e.g., a 266nm, 355 nm, 532 nm, or 1064 nm laser beam), an ion beam, an electronbeam, and an arc discharge. Without wishing to be bound by theory, it isbelieved that the plasma thus formed contains various excited atomicelements, which emit electromagnetic radiations when these atomicelements return to a lower energy state. In some embodiments, theelectromagnetic radiations are indicative of atomic emissions. In someembodiments, the electromagnetic radiations can further include thoseindicative of one or more of isotopic emissions, molecular emissions,molecular isotopic emissions, and spectral interference between atomicemissions from different atoms in the sample.

As used herein, the phrase “atomic emission” refers to emission of anelectromagnetic radiation by an atomic element (e.g., a metal elementsuch as Na or Mg) in a sample. Conventionally, atomic emissions havebeen used as the primary signals in an element analysis measurement todetermine the place of origin of a sample, while the other emissions(e.g., isotopic emissions or molecular emissions) have been generallydiscarded as noise. For example, a conventional method typically selectsa part of an emission spectrum (e.g., by using algorithms such asPartial Least Squares (PLS) or Principle Component Analysis (PCA)), oraverage a number of spectra to reduce what is assumed to be noise. Bycontrast, the methods disclosed herein rely on the entire emissionspectra acquired from a sample to determine its place of origin. Withoutwishing to be bound by theory, it is believed that the emissions otherthan atomic emission (e.g., isotopic emissions, molecular emissions, ormolecular isotopic emissions) represent “sequences” (or a petrogeneticsignature) of a reference or unknown sample and should be included inthe data analyses described herein to determine the place of origin ofunknown samples. Further, without wishing to be bound by theory, it isbelieved that the minerals of the same type can have different“sequences” that vary depending on their places of origin (e.g., fromcountries to countries, from deposits to deposits, from mines to mines,and from zones to zones) and the environmental conditions (e.g.,weathering, hydrothermal alternation, and local tectonic stresses) oftheir places of origin. Thus, without wishing to be bound by theory, itis believed that by using the entire emission spectra acquired from asample (which can include the above-discussed additional emissions, aswell as spectral interferences among these emissions and potentiallyother yet unidentified features), one can determine the place of originof an unknown sample more accurately than conventional methods.

As used herein, the phrase “molecular emission” refers to emission of anelectromagnetic radiation by a molecule (e.g., H₂O or CO₂) in a sample.The phrase “isotopic emission” refers to emission of an electromagneticradiation by an isotope of an atomic element (e.g., deuterium andhydrogen, ²³⁵U/²³⁸U, or ¹⁰B/¹¹B) in a sample. Isotopic emissions withinspectra are generally small. For example, the isotopic shift between²³⁵U/²³⁸U at the emission line at 424.412 nm is 0.025 nm. As anotherexample, the isotopic shift between ²³⁹Pu and ²⁴⁰Pu at the emission lineof 594.522 nm is 0.005 nm and the isotopic shift between ¹⁰B and ¹¹B atthe emission line of 208.889 nm is 0.002 nm. It has been shown by LaserAblation Molecular Isotopic Spectrometry (LAMIS) that the isotopicshifts found in molecular spectra are significantly larger than those ofisotopic (atomic) spectra. For example, the molecular isotopic shift for¹⁰B¹⁶O and ¹¹B¹⁶O is 0.73 nm, which is significantly larger than theisotopic shift for ¹⁰B and ¹¹B when they are not bonded to O. Theseisotopic shifts and molecular isotopic shifts are usually so smallrelative to the total intensity of the emitted radiation from a samplethat they are traditionally disregarded as noise. However, as themethods disclosed herein utilize the entire emission spectrum of asample, these small shifts are retained during data analyses whencomparing the spectra collected from an unknown sample to the referencesamples in a library.

As used herein, the phrase “spectral interference” refers to incompleteisolation of the radiation emitted by an analyte from other radiationsdetected by an instrument. As an example, when using the methodsdisclosed herein to analyze the mineral beryl, the element Be can havespectral interference with V, Ti, Fe, Cr, Mg, and Mn, the element Al canhave spectral interference with Mg, V, Ca, Ti, Cr, Fe, and Mn, and theelement Si can have spectral interference with Cr, Fe, Mg, V, Al, andMn. These spectral interferences may cause an inaccurate representationof the chemical composition of the sample tested. Thus, traditionally,spectral interferences can be problematic in analyzing the chemicalcomposition of a sample (especially in a quantitative measurement). Bycontrast, when used to determine the place of origin of an unknownsample, the methods disclosed herein can utilize the informationcontained in the spectral interference as these methods involvecomparing the entire spectra acquired from the unknown sample with theentire spectra acquired from each of the reference samples in a library,and therefore are not concerned about the absolute intensities of theradiations emitted from the reference and unknown samples.

In general, step (a) in the methods disclosed herein includesirradiating a sample with electromagnetic radiation (e.g., laser)multiple times (e.g., at least 20 times, at least 30 times, at least 40times, at least 60 times, or at least 80 times). In some embodiments, asample is irradiated with electromagnetic radiation at multiplelocations (e.g., at least 10 locations, at least 15 locations, at least20 locations, at least 30 locations, at least 40 locations, at least 60locations, or at least 120 locations) and up to as many as 240 locationsor more. In some embodiments, a sample is irradiated withelectromagnetic radiation multiple times at each of the above locations(e.g., at least twice or at least three times at each location). Withoutwishing to be bound by theory, it is believed that a sample (e.g., amineral) can be heterogeneous both laterally and vertically on amicroscopic scale. In addition, different pulses of electromagneticradiation can have different energy intensities and therefore canproduce different emission spectra. Thus, without wishing to be bound bytheory, it is believed that by irradiating a sample at multiplelocations and multiple times at each of these multiple locations andthen collecting the spectra produced by these irradiations, one cancapture a more complete picture of the above variations, which arecharacteristic of the place of origin of a sample. Thus, by using themethods disclosed herein, one can determine the place of origin of asample more accurately (e.g., pinpointing the particular deposit or minefrom which the sample is obtained).

In general, the irradiation locations are spaced from each other at asuitable distance (e.g., to be sufficiently large to characterizeheterogeneity in the atomic composition of a sample or to make sure thesample from each location is not contaminated with the debris producedfrom previous irradiations). In some embodiments, the suitable distancecan be at least 10 μm (e.g., at least 15 μm or at least 20 μm). Incertain embodiments, the suitable distance can be at least 100 nm (e.g.,at least 1 μm or at least 5 μm).

One can generally carry out step (b) by recording a spectrum ofelectromagnetic radiation emitted in response to each of the sampleconversions (i.e., to form a plasma). In some embodiments, each spectrumis first detected by a detector (e.g., a spectrometer) and then recordedin an electronic processor (e.g., a computer). As a sample is irradiatedwith electromagnetic radiation (e.g., a laser) multiple times (e.g., atleast 60 times) in step (a), multiple spectra are obtained from thesample. In general, each spectrum is detected and recorded prior to thenext sample conversion by irradiation with electromagnetic radiation. Insome embodiments, each spectrum is recorded with a spectral resolutionsufficient to resolve the emission of electromagnetic radiationcorresponding to atomic emission and one or more of isotopic emission,molecular emission, molecular isotopic emission, and spectralinterference between atomic emissions from different atoms. In someembodiments, suitable spectral resolution can be at least 10,000channels (e.g., at least 20,000 channels, at least 30,000 channels, atleast 40,000 channels, at least 60,000 channels, at least 80,000channels, at least 100,000 channels, at least 200,000 channels, or atleast 300,000 channels) and up to as many as 400,000 channels or more.For example, a suitable spectral resolution can be 40,000 or 67,000channels. Without wishing to be bound by theory, it is believed thatusing a high spectral resolution in the methods disclosed herein canresolve fine spectral lines or bands and therefore can increase theaccuracy of the final results. For example, when a spectral resolutionof as many as 400,000 channels is used in a spectral window between 195nm and 1005 nm, spectral lines or bands having a width of about 2 pm canbe resolved.

In some embodiments, after all spectra of a sample are recorded, thespectra can be scaled to a common unit of measurement. The scaling isgenerally achieved by using a piece of information preservingtransformation (e.g., by dividing each spectrum channel by the meanvalue of the energy used to generate the spectra). The scaled spectracan then be compared among themselves to determine the number ofdifferent spectra (also referred to as “constituent signals”). Thecomparison can be performed (e.g., by an electronic processor such as acomputer) as follows: One can first compare first and second scaledspectra from a sample. If these two scaled spectra are sufficientlysimilar, they are considered to be the same spectrum (i.e., the sameconstituent signal). If these two scaled spectra are substantiallydifferent, they are considered to be two different spectra (i.e., twodifferent constituent signals). In some embodiments, to determine thesimilarity of two scaled spectra, one can use a matching algorithm(e.g., a weighted K-nearest neighbor algorithm) to compare the entiresequence of spectra for a sample to obtain a common reference spectrum(e.g., a centroid spectrum for all of the spectra for the sample). Onecan then compute the difference between each spectrum and the commonreference spectrum. Subsequently, one can calculate the standarddeviation of all of the differences. Two spectra are considered to besimilar if the difference between each spectrum and the common referencespectrum is less than a given percent of the standard deviation of thedifferences from the common reference spectrum. One can then compare thethird scaled spectrum with the first two scaled spectra. If the thirdscaled spectrum is sufficiently similar to one of the first and secondscaled spectra, the third scaled spectrum is not considered to be aunique spectrum. If the third scaled spectrum is substantially differentfrom either of the first and second scaled spectra, the third scaledspectrum is considered to be a unique spectrum (i.e., a differentconstituent signal). The process can be repeated until all scaledspectra collected from a sample have been compared with the other scaledspectra from the same sample. The unique spectra (each of which is inresponse to a different one of the sample conversions) can then becompiled to form a set of constituent spectra for the sequence. Eachunique spectrum in a sequence is also referred to hereinafter as “amember” of the sequence. In general, a sequence can include one member(e.g., if the sample is perfectly homogenous) or two or more members(e.g., if the sample is heterogeneous). For many samples, the sequencetypically includes at least 10 members (e.g., at least 15 members or atleast 64 members).

Cluster technique algorithms such as those commercially available inMATLAB® toolboxes from MathWorks Inc. (Natick, Mass.) can be used todetermine the constituent spectra in a sequence of spectra. For example,the weighted K-nearest neighbor algorithm described above can be used toidentify spectra that define a common constituent when the weightedK-nearest neighbor differences are small enough.

In some embodiments, certain members of a sequence for a samplecorrespond to the spectra recorded in response to different parameters(e.g., different pulse energies, different pulse durations, differentpulse wavelengths, or combinations thereof) for the pulses ofelectromagnetic radiation used to convert a portion of the sample intothe plasma during the multiple conversions performed in step (a).

In some embodiments, certain members of a sequence for a samplecorrespond to the spectra recorded in response to different incidentlocations on the sample at which the pulse of electromagnetic radiationis irradiated to convert a portion of the sample into the plasma duringthe multiple conversions performed in step (a). In some embodiments, thedifferent incident locations are sufficient to characterizeheterogeneity in the atomic composition of a sample. For example, thedifferent incident locations can be separated from one another by atleast 10 μm (e.g., at least 15 μm or at least 20 μm). In someembodiments, certain members of a sequence for a sample correspond tothe spectra recorded in response to different parameters for the pulsesof electromagnetic radiation and different incident locations.

It is important to note that the methods disclosed herein utilize theentire spectrum of each spectrum in the sequence for a sample (e.g., areference or unknown sample) without smoothing the spectrum or reducingthe noise in the spectra by averaging spectra obtained from differentirradiations and/or averaging spectra obtained from differentirradiation locations and/or discarding low amplitude spectral lines andbands as noise. Without wishing to be bound by theory, it is believedthat all of such information obtained from a sample is important, as itrepresents the “sequence” of the sample, and can be used to determinethe place of origin of an unknown sample more accurately. By contrast, aconventional method generally uses only a portion of a spectrum from asample, or averages a number of spectra to reduce noise, or discards lowamplitude spectral lines as noise, which would lose valuable informationabout the sample.

After the sequence of spectra for a sample is obtained, step (c) can beperformed by comparing the sequence with a sequence of spectra for eachof multiple reference samples in a reference library. In general, todetermine the place of origin of an unknown sample, the place of originof each reference sample in the library is known.

Generally, the measured spectra of a sample are also scaled to a commonunit of measurement by using a piece of information preservingtransformation (e.g., by dividing each spectrum channel by the meanvalue of the pulse energy used to generate the spectra). This scaling isisomorphic so as to preserve relative variations within each spectrum.

In general, to establish a reference library for a geological material(e.g., a gemstone or a metal), reference samples can first be collectedfrom deposits all around the world. To ensure a high degree ofconfidence in the place of origin of the geological material, it isdesirable to document sufficient information about the material sampleduring collection, such as (1) the GPS coordinates of the location, (2)the time and date of collection, (3) the name and affiliation of thecollector, (4) whether the sample is extracted from weathered rock, (5)whether the sample is extracted directly from a host rock, (6) the zonefrom which the sample is extracted, (7) the type of host rock, (8)whether the sample is extracted from mine tailings, the floor of themine, or a river, (9) a description of the physical sample (e.g., itscolor, size, inclusions, or host rock), and (10) whether the sample iscollected with other samples. In some embodiments, it is desirable tocollect a statistically significant number of samples (e.g., at least 30samples) from a particular mine in a deposit. In some embodiments, if adeposit has multiple mines, it is desirable to collect a statisticallysignificant number of samples (e.g., at least 30 samples) from eachmine. In some embodiments, if a mine has multiple zones containing thesame geological material (e.g., a pegmatite), it is desirable to collecta statistically significant number of samples (e.g., at least 30samples) from each zone. After collection, all samples are assigned aninternal tracking number that can be used to track the samples to thecollection event. The documents describing the parameters of collectionare preserved with the physical samples, and rigorous chain-of-custodyprocedures are followed to ensure continuing integrity of the referencecollection.

A sequence of spectra of each of the collected reference samples canthen be obtained by carrying out steps (a) and (b) described above. Insome embodiments, after the spectra of all collected reference samplesare obtained, one can then apply a data analysis process (e.g., amatching algorithm such as a weighted K-nearest neighbor algorithm) tothe reference samples to determine how similar/dissimilar the referencesamples are to each other. In some embodiments, the weighting used in aweighted K-nearest neighbor algorithm is determined by a kernel densityestimation function, such as that described in Webb, Statistical PatternRecognition, John Wiley & Sons, 2002. In some embodiments, the dataanalysis process takes into account the distance between the data fromtwo irradiation locations, where distance can be measured by theMahalanobis metric, such as that described in Warren et al., Use ofMahalanobis Distance for Detecting Outliers and Outlier Cluster inMarkedly Non-Normal Data, NTIS Technical Report, 2011. Other aspects ofthe weighted K-nearest neighbor algorithm can be found, for example, inViswanath, et al., An improvement to k-nearest neighbor classifier,2011, arXiv: 1301.6324. In some embodiments, the data analysis processcan start with a fixed set of test parameters (e.g., the number of datachannels, the number of sample groups, the number of samples in eachgroup, the distance between irradiation locations, the number of gridpoints (irradiation locations) in each sample, the shape of the grid asdefined by the number of X-coordinates, y-coordinates and z-coordinates,the weights to be used in the weighting of the K-nearest neighboralgorithm, and/or the percent of the standard deviation of thedifferences between each spectrum and the common reference spectrum) andthen start testing each reference sample at various incident locationson or in the sample. At the completion of this data analysis process, aprofile of each test with respect to all of the other tests on or inthat sample is defined. After the data analysis process is completed forall reference samples, the data obtained are included in a database.Analysis of the database can then be performed (e.g., by using amatching algorithm such as a weighted K-nearest neighbor algorithmdescribed above) to determine how similar/dissimilar the referencesamples are to each other. For example, if test results obtained fromthe surface of some of the samples are not similar to those obtainedfrom the surface tests of the rest of the samples while test resultsobtained from a sub-surface in the samples are similar for all of thesamples, one can conclude that there are two different types of coatingson the tested samples or that some of the tested samples lack a coatingif test results obtained from the sub-surface and surface are similarfor that set of samples. Based on the above analysis, samples having acommon attribute (e.g., the same place of origin) can be included in areference group.

In some embodiments, a single sample among a group of samples supposedlyfrom the same place of origin may have very little similarity to theother samples in the group. This can imply that the assumption that thissample shares a common attribute with the other samples in the group maybe false and may require further investigation before including thissample in a reference group. For example, the above incident can becaused by a human error (e.g., by misplacing a sample from a differentlocation in that group).

In some embodiments, the above data analysis process can be applied todifferent reference groups in a reference library to determine theinherent dissimilarity between the groups. In general, no assumptionsare made about the size or number of the reference groups that make upthe reference library other than that each reference group has someattribute(s) that make it distinct from other groups. For example, thereference library can include only two reference groups, with one grouphaving reference samples with a desirable property and the other grouphaving reference samples lacking that desirable property. As anotherexample, a reference library can include a large number of referencegroups. For example, a reference library can be composed of emeraldsamples that are grouped based on the countries they came from.

In some embodiments, after a reference library is created, the sequenceof spectra of an unknown sample can be compared with the sequence ofspectra for each of the reference samples in the reference library(e.g., by using a data analysis process such as a weighted K-nearestneighbor algorithm) to determine whether the sequence of the unknownsample is similar to those of the samples in a reference group in thelibrary. Suitable data analysis processes are discussed above. Based onthe comparison, step (d) can be performed by using an electronicprocessor (such as a computer) to determine certain information aboutthe sample. For example, if the sequence of the unknown sample issubstantially similar to the sequence of one or more samples in aparticular reference group, it can be concluded that the unknown samplebelongs to this group. As another example, if the sequence of theunknown sample is significantly different from the samples in all of thereference groups in a library, it can be concluded that the unknownsample belongs to a new group not already in the library.

As noted above, in certain embodiments, the reference library caninclude only one reference sample. In such embodiments, the methodsdisclosed herein can be used to verify whether the unknown sample is thesame as, or different from, the reference sample (e.g., for verificationor authentication applications).

In some embodiments, after a reference library composed of mineralsamples grouped based on their places of origin is created, the place oforigin of an unknown sample of the same mineral can then be determinedby comparing its sequence with the sequence of spectra for each of thereference samples in the reference library. In some embodiments, thecomparison can be performed (e.g., by an electronic processor) toidentify a degree to which the sequence of the sample matches a sequencefor each of at least some of the reference samples in the library. Forexample, FIG. 2 is a flow chart showing a series of exemplary steps ofperforming this comparison. As shown in FIG. 2, the comparison can beperformed by (a) comparing each spectrum in the sequence for the sampleto the different spectra in the library to identify the differentspectra from the library most likely to match the spectra in thesequence for the sample; (b) identifying which reference samples fromthe library include all of the identified spectra; and (c) identifying adegree to which the sequence for the sample matches a sequence for eachof the identified reference samples. The above comparing and identifyingstep can be performed by using a data analysis process (e.g., theweighted K-nearest neighbor algorithm described above).

In some embodiments, the comparison by the electronic processor betweenthe sequence of spectra for the sample being analyzed and the sequenceof spectra for each of the reference samples includes comparing aprobability distribution for the members of sequence of spectra in thesample being analyzed to a probability distribution for the members ofthe sequence of spectra for each of the reference samples. For example,the probability distribution for the sample being analyzed can berepresented as a histogram indicating the number of times each memberoccurs in the sequence of spectra for the sample being analyzed and theprobability distribution for the members of each reference sample can berepresented as a histogram indicating the number of times each memberoccurs in the sequence of spectra for each reference sample. Theelectronic processor can then determine whether the sample beinganalyzed is one of the reference samples based on the degree to whichthis probability distribution for the sample being analyzedsubstantially matches this probability distribution for any one of thereference samples.

This algorithm is shown schematically in FIG. 3 according to thefollowing steps: (a) construct a probability distribution for themembers of the sequence of spectra for the sample being analyzed; (b)for each reference sample in the library, construct a probabilitydistribution for the members of the sequence of spectra for thatreference sample; (c) compare the probability distribution for thesample being analyzed to the probability distribution for each of thereference samples in the library; and (d) identifying a degree to whichthe probability distribution for the sample being analyzed is similar tothe probability distribution for any of the reference samples.

Without wishing to be bound by theory, it is believed that each mine ordeposit has a unique petrogenetic signature, i.e., elemental andisotopic ratios unique to the petrogenesis of the deposit, and thatreference samples from the same mine or deposit have a similarpetrogenetic signature. Further, without wishing to be bound by theory,it is believed that one advantage of the methods disclosed herein isthat the entire emission spectrum (including atomic emissions, isotopicemissions, molecular emissions, molecular isotopic emissions, andspectral interference between atomic emissions from different atoms) inresponse to each irradiation of a sample with electromagnetic radiation(e.g., laser) is utilized in the above data analysis to determine thesimilarity/differences between the samples in a library since only theentire emission spectrum of a sample can include all of the informationin the petrogenetic signature of the sample. As a result, the methodsdisclosed herein can create a reference library containing referencesamples with more precise location information and can identify theplace of origin of an unknown sample more accurately than conventionalmethods, which typically use a selected window of spectrum, use atomicemissions only, or use averaged spectra to identify the place of originof an unknown sample.

FIG. 4 illustrates an exemplary system of acquiring and analyzing anemission spectrum of a sample, which can be used to perform the methodsdiscussed above (e.g., creating a reference library of various referencesamples or determining the place of origin of an unknown sample). Asshown in FIG. 1, system 10 includes a sample 20, an excitation source30, a detector 40, and an electronic processor 50. Sample 20 can bethose described above. Examples of excitation source 30 can be a laser(e.g., a Nd:YAG laser), an ion beam source (e.g., a liquid-metal ionsource), an electron beam source, or an arc discharge lamp. Detector 40can be a spectrometer (e.g., an Echelle spectrometer).

Although FIG. 4 depicts a certain geometric arrangement for excitationsource 30 and detector 40 relative to sample 20, this is only by way ofexample. Accordingly, many different arrangements are possible for therelative positioning of excitation source 30, detector 40, and sample 20as long as system 10 can acquire the emission spectra of sample 20. Forexample, in some embodiments, excitation source 30 can be placed at alocation so that the incident irradiation is at a 90° angle to sample 20and the emission from the sample is collected at a 45° angle from sample20.

Electronic processor 50 can include one or more programmable computersand/or preprogrammed integrated circuits. It can further include one ormore data storage systems (e.g., a memory and/or a storage element), oneor more input devices (e.g., a keyboard), and one or more output devices(e.g., a display or a printer). Electronic processor 50 is generallydesigned to execute programs based on standard programming techniques.System 10 can also include other components (not shown in FIG. 1), suchas a sample holder or a sample stage (with the capability ofthree-dimensional movement) and a camera (e.g., an ICCD camera).Furthermore, in some embodiments, some or all of the components ofelectronic processor 50 are directly coupled to detector 40. In otherembodiments, some or all of the components of electronic processor 50are physically separated from detector 40. For example, some or all ofthe processing can be carried out among one or more distributedprocessors that are located far from detector 40 (e.g., in the “cloud”).

In some embodiments, the methods discussed above can be carried out byfirst emitting a pulse of electromagnetic radiation 12 (e.g., a highpower laser pulse) from excitation source 30 to irradiate an incidentlocation on sample 20 to create a plasma 16, which includes excitedatomic elements. This step can be performed at multiple incidentlocations and/or multiple times at each incident location.Electromagnetic radiation 14 emitted from plasma 16 can then becollected (e.g., through fiber optics or a collimating lens) anddetected by detector 40. The signals received from detector 40 can thenbe forwarded to electronic processor 50 to be recorded as emissionspectra of the sample and analyzed (as described above) to determine theplace of origin of the sample (e.g., by comparison to the emissionspectra of reference samples in a reference library).

In some embodiments, the system shown in FIG. 1 can be a laser-inducedbreakdown spectroscopy (LIBS) system. In such embodiments, excitationsource 30 can be a laser. In general, the laser has a sufficiently highenergy to convert a portion of sample 20 to plasma 16. In someembodiments, the laser has a pulse energy of at least about 10 mJ (e.g.,at least about 12 mJ, at least about 14 mJ, or at least about 16 mJ)and/or at most about 250 mJ (e.g., at most about 200 mJ, at most about180 mJ, at most about 160 mJ, at most about 140 mJ, at most about 120mJ, at most about 100 mJ, at most about 80 mJ, at most about 60 mJ, atmost about 40 mJ, or at most about 20 mJ). In some embodiments, thepulse energy for each irradiation to sample 20 is substantially thesame.

In some embodiments, the laser has a pulse duration of about 0.1 ps(e.g., at least about 1 ps, at least about 10 ps, or at least about 100ps) and/or at most about 10 ns (e.g., at most about 5 ns, at most about1 ns, or at most about 0.5 ns). In some embodiments, the pulse durationfor each irradiation to sample 20 is substantially the same.

In some embodiments, a LIBS system can include two lasers with differentwavelengths. For example, a LIBS system can include a 266 nm laser and a1064 nm laser. Without wishing to be bound by theory, it is believedthat the 266 nm laser can be used for analyzing transparent samples asit minimizes the traces or damage of testing to the samples and the 1064nm laser can be used for analyzing translucent and opaque samples as itcouples better with the surface of such a sample.

In some embodiments, a LIBS system can include a detector (e.g., aspectrometer) with sufficiently high spectral resolution andsufficiently wide spectral window. In some embodiments, the detector hasat least 10,000 channels (e.g., at least 20,000 channels, at least30,000 channels, at least 40,000 channels, at least 60,000 channels, atleast 80,000 channels, at least 100,000 channels, at least 200,000channels, or at least 300,000 channels) and up to as many as 400,000channels or more. In some embodiments, the detector can have 40,000 or67,000 channels. In some embodiments, the detector can resolve featuresor peaks finer than 0.1 nm (e.g., finer than 0.06 nm or finer than about0.02 nm). In some embodiments, the detector can have a spectral windowbetween 195 nm and 1005 nm. For example, with as many as 400,000channels, the spectral resolution is finer than about 2 pm over thespectral window between 195 nm and 1005 nm.

Other components in a LIBS system are generally known in the art, suchas those described in U.S. Pat. Nos. 5,751,416; 7,195,371; and7,557,917; Cremers et al., Handbook of Laser-Induced BreakdownSpectroscopy, John Wiley & Sons Ltd, 2006; and Miziolek et al.,Laser-Induced Breakdown Spectroscopy (LIBS) Fundamentals andApplications, Cambridge University Press, 2006.

In general, LIBS systems offer various advantages over other analyticaltechniques for determining the place of origin of an unknown sample. Forexample, LIBS systems are easy to use (e.g., requiring minimal samplepreparation) and relatively inexpensive. In addition, LIBS systems canbe portable so they can be used outside of a laboratory (e.g., at afield site). LIBS systems are available from commercial sources, such asPhoton Machines, Inc. (Redmond, Wash.) and Applied Spectra (Fremont,Calif.).

In some embodiments, a LIBS system can be used to analyze a reference orunknown sample as follows. Prior to a sample being analyzed, the laserin the LIBS system is generally warmed up (e.g., by irradiating pulsesof laser without using the emitted radiation for analysis) for asufficient period of time (e.g., at least 10 minutes). After the laseris warmed up, a calibration sample can be analyzed to ensure therepeatability of results (e.g., from day to day). All data collected oncalibration samples can be saved along with photos of the calibrationsample. If the analysis of the calibration sample does not fall withintolerated levels, a diagnostic test can be performed to ensure the LIBSsystem is working correctly. If the analysis of the calibration samplefalls within tolerated levels, the analysis of a reference or unknownsample can begin.

In general, sample 20 is cleaned prior to being analyzed by a LIBSsystem. For example, sample 20 can be cleaned by a medical alcohol wipe,and followed by washing with an alcohol (e.g., isopropyl alcohol). Aftersample 20 is cleaned, it can be mounted on a sample stage in a mountingmaterial (e.g., a mineral tack) for testing in a LIBS system. Themounting material is generally changed when a different sample isanalyzed to reduce the risk of cross contamination.

Once a sample is cleaned and mounted, it can be brought into focus onthe sample stage. In some embodiments, when the sample is optically infocus on a computer monitor connected to a camera in the LIBS system, itis also in focus for the laser beam to form a plasma. Prior toirradiating a laser pulse to the sample to generate a plasma, a flow ofa high purity inert gas (e.g., 99.999% pure argon) can be turned on tocover the surface of the sample to be analyzed. Without wishing to bebound by theory, it is believed that, due to extremely small variationspresent in geological materials, using a pure inert gas (e.g., argon) tocover the sample surface can avoid contamination and variability fromatmospheric air.

In certain embodiments, system 10 includes an adjustable iris to controlthe spatial profile of radiation 12 produced by source 30. For example,the iris can be used to adjust a beam diameter of radiation 12,spatially filtering certain portions of the beam to generate a filteredoutput beam. FIG. 6 shows a schematic diagram of system 10 that includesan adjustable iris 60 which is coupled to electronic processor 50. Asshown in FIG. 6, iris 60 filters radiation 12 before the radiation isincident on sample 20.

During use, system 10 can be configured to automatically correct theposition of iris 60 if the iris falls out of alignment with source 30.Correction of the position of iris 60 can be initiated by electronicprocessor 50 upon detection of a triggering event, and/or as part of aroutine calibration of system 10, which can occur at periodic intervals.In either circumstance, electronic processor 50 is typically configuredto determine whether adjustment of iris 60 is needed based onmeasurement of emitted radiation 14 from sample 20.

For purposes of calibration, in some embodiments, system 10 can includea dedicated calibration sample. In FIG. 6, for example, a calibrationsample 20 is mounted to a support 55, which is connected to processor 50via control line 57. To initiate calibration of system 10, processor 50delivers a control signal to support 55 which causes support 55 toposition the calibration sample in the path of radiation 12 from source30. Where system 10 includes an automated sample positioning system (aswill be described later), processor 50 can also deliver a control signalif necessary to the automated sample positioning system to withdraw anysample from the path of radiation 12.

Support 55 can generally be implemented in a variety of ways. In someembodiments, for example, support 55 is implemented as an articulatingarm on which the calibration sample is mounted. In some embodiments,support 55 is implemented as a rotating disc that includes a solidportion on which the calibration sample is mounted, and a largeaperture. During calibration, the disc is rotated so that thecalibration sample is positioned in the path of radiation 12. Duringsample measurements, the disc is rotated so that the aperture allowsradiation 12 to pass through the disc and be incident on the sample.

System 10 can generally be configured to perform calibration under avariety of conditions. In some embodiments, for example, system 10performs a calibration routine upon system start-up and/or upon systemshutdown. In certain embodiments, system 10 performs a calibrationroutine after having been in operation for a certain time period and/orafter having analyzed a certain number of samples. In some embodiments,a calibration routine can also be performed when positions of any of thesystem components have changed, when the sample has changed, and/or uponreceiving a signal from a system user.

To check whether the alignment of iris 60 is within tolerance,electronic processor 50 typically delivers 64 laser shots in an 8×8 gridpattern to the surface of sample 20, and measures (using detector 40)radiation 14 emitted following each shot. Processor 50 then calculatesthe average maximum signal intensity for the emitted radiationcorresponding to the 64 shots, and compares the average maximum signalintensity to a threshold intensity value. If the average maximum signalintensity falls below the threshold value, processor 50 determines thatthe alignment of iris 60 should be checked.

To check the alignment of iris 60, processor 50 delivers a controlsignal to an actuator within iris 60 through control line 62. Thecontrol signal causes the actuator to displace iris 60 from its originalposition by a fixed amount along a first axis. Then, processor 50activates source 30 to deliver an additional 8×8 pattern of 64 lasershots to sample 20, measures emitted radiation 14 following each shot,and calculates the average maximum signal intensity for the 64 shots. Ifthe average maximum signal intensity has increased relative to the priormeasurement, processor 50 delivers another control signal to theactuator within iris 60 to displace the iris in the same direction. Ifinstead the average maximum signal intensity has decreased relative tothe prior measurement, processor 50 delivers another control signal tothe actuator to displace the iris in the opposite direction. Thisprocess of measurement and displacement is repeated until the irislocation along the first axis at which the average maximum signalintensity is highest is determined. Once determined, processor 50positions iris 60 at this location.

In certain embodiments, processor 50 repeats this iterative alignmentcheck for a second axis, which is typically perpendicular to the firstaxis (and the first and second axes are typically both orthogonal to apropagation direction of radiation 12 produced by source 30). In thismanner, iris 60 can be positioned or re-positioned at an optimizedlocation in a plane transverse to the propagation direction of radiation12.

The threshold value of the average maximum signal intensity that is usedto determine whether the alignment of iris 60 should be checked dependsupon a variety of factors, including the configuration of detector 40and the nature of sample 20. In some embodiments, for example, thethreshold value corresponds to between 123,000 and 128,000 detectorcounts.

In some embodiments, processor 50 is configured to also check thediameter of iris 60. The diameter of iris 60 can be checked based on thesame triggering event (e.g., a measurement of average maximum signalintensity that falls below an established threshold) that triggers acheck of the iris alignment. For example, after optimizing the locationof iris 60, processor 50 can deliver a control signal to a secondactuator in iris 60 that causes the actuator to increase the diameter ofiris 60. Processor 50 then activates source 30 to deliver a further 8×8pattern of 64 laser shots to the surface of sample 20, calculates anaverage maximum signal intensity corresponding to the 64 shots, andcompares the average maximum signal intensity to an established targetrange for the signal intensity. If the calculated intensity is largerthan the target range, processor 50 delivers a control signal to thesecond actuator causing it to reduce the diameter of iris 60. If thecalculated intensity is smaller than the target range, processor 50delivers a control signal to the second actuator causing it to increasethe diameter of iris 60. Processor 50 then repeats the steps describedabove in an iterative manner until the calculated average maximum signalintensity falls within the target range, thereby ensuring that thediameter of iris 60 falls within established tolerances. In certainembodiments, system 10 is also configured to check the alignment and/orcondition of various optical elements in the system. For example,referring to FIG. 6, system 10 can include an objective lens 70positioned in a mount with actuators that displace lens 70 in twoorthogonal directions in a plane transverse to the propagation directionof radiation 12. As part of a calibration procedure and/or after atriggering event, processor 50 can be configured to check the alignmentof objective lens 70.

The procedure for checking the alignment of lens 70 is similar to theprocedure for checking the alignment of iris 60. As described above, anexample triggering event can be measurement of an average maximum signalintensity for an 8×8 grid pattern of laser shots that falls below athreshold value (e.g., the same threshold value that triggers the checkof the iris alignment). Typically, processor 50 first checks andoptimizes alignment of iris 60. Then, processor 50 checks and optimizesthe alignment of lens 70 in a similar manner, first along one transverseaxis, and then along the other.

In certain cases, after alignment of both iris 60 and lens 70, thecalculated average maximum signal intensity may still fall below thethreshold value. This can occur, for example, if lens 70 is contaminatedor damaged. When this condition occurs, processor 50 can deliver avisual and/or audio warning to a user of system 10 that the measuredsignal level is out of tolerance, and that the optical components of thesystem (e.g., lens 70) should be visually inspected.

To further protect the optical elements of system 10 againstcontamination and damage, and to reduce the effects of environmentalconditions (e.g., humidity, temperature fluctuations, dust) on themeasurement results, some or all of the system optics can be enclosedwithin a housing. Referring again to FIG. 6, in some embodiments, system10 can include a housing 80 that extends along a portion, or all, of thepath of radiation 12. In general, housing 80 can enclose some or all ofthe components of system 10, and can be fixed or can be movable withrespect to the other components of system 10.

For example, in FIG. 6, housing 80 is a movable housing that enclosesiris 60 and lens 70. Housing 80 is connected to an actuator 84, which iscoupled to electronic processor 50 via control line 86. Processor 50, bydelivering suitable control signals to actuator 84, can extend andretract housing 80 in a direction parallel to arrow 88. For example,during positioning of sample 20, housing 80 is typically in a retractedposition. Once the position of sample 20 has been fixed, housing 80 isthen extended by processor 50 until the housing is close to, or evencontacts, the surface of sample 20. A sensor 89 mounted on housing 80and coupled to processor 50 alerts processor 50 when housing 80 contactsthe surface of sample 20, so that processor 80 halts extension ofhousing 80. Housing 80 can also include an opening 82 that allowsemitted radiation 14 from sample 20 to exit the housing and be detected.

Housing 80 can generally be formed from a variety of differentmaterials, such as various plastics. It can be advantageous for certainapplications if housing 80 is formed from a material that is partiallyor completely transparent. Accordingly, in some embodiments, housing 80can be formed from a clear plastic material such as Lucite®.

In some embodiments, detector 40 can include an adjustable fiber opticcollector for collecting emitted light 14 from sample 20. Samples 20 cantypically be of significantly different size, and proper positioning ofsample 20 with respect to detector 40 is an important consideration.Further, the alignment of detector 40, including its fiber opticcollector, can change due to a variety of factors including mechanicalvibrations, heating and cooling cycles, changes in ambient humidity, andaccidental handling errors.

FIG. 7 shows a schematic diagram of detector 40 and fiber opticcollector 90. To permit reproducible positioning and re-positioning offiber optic collector 90, system 10 can include an actuator 98 coupledto processor 50 that positions fiber optic collector 90 with respect tosample 20. In general, processor 50 can be configured to position fiberoptic collector 90 using the same iterative procedure described above inconnection with the optimization of iris 60 and lens 70. In general, theposition of fiber optic collector 90 is optimized in three-dimensionalspace with respect to the position of sample 20, using the calculationof average maximum signal intensity as feedback to guide the iterativeadjustment of fiber position. In some embodiments, fiber optic collector90 can include a sensor 99 that alerts processor 50 when fiber opticcollector 90 contacts sample 20 (or housing 80), so that the process ofpositioning collector 90 does not damage or cause mis-alignment ofcollector 90 or other system components.

As discussed above in connection with iris 60 and lens 70, the positionof collector 90 can be checked at certain intervals (e.g., as part of aregular calibration routine) and/or when a triggering event is detected,such as a low measured value of average maximum signal intensity. Inaddition, collector 90 can be re-positioned when sample 20 is changed.In general, when re-positioning is performed as part of a calibrationroutine or as a result of a triggering event, the position of collector90 is checked and adjusted before the positions of iris 60 and/or lens70 are checked.

In certain embodiments, fiber optic collector 90 can include multipleports. For example, collector 90 in FIG. 7 includes a measurement port92 and a calibration port 94 joined together at fiber splice 95.Measurement port 92 collects emitted radiation 14 from sample 20, asdiscussed above. Calibration port 94 is coupled to calibration source93, e.g., a mercury vapor lamp. When the spectral response of detector40 is being calibrated by system 10, processor 50 activates calibrationsource 93, which generates calibration light that is coupled intodetector 40 through calibration port 94. When system 10 is measuringemitted radiation 14 from sample 20, the emitted radiation is coupledinto detector 40 through measurement port 92.

The use of multiple fiber optic ports in fiber optic collector 90 canhave important advantages. For example, by using different ports forcalibration and measurement of emitted radiation, calibration source 93does not have to be mounted and de-mounted from collector 90. Theprocess of mounting and de-mounting calibration source 93 is timeconsuming, prone to causing fiber mis-alignment, and can potentiallyweaken the connections between components even via normal handling. Byfixing the connection between calibration source 93 and calibration port94, these factors are significantly mitigated.

In general, collector 90 can include any number of fiber ports.Additional fiber ports can be used, for example, to connect othercomponents to detector 40, such as other calibration sources.Alternatively, or in addition, collector 90 can include more than onemeasurement port in some embodiments.

Other arrangements can also be used to couple light from calibrationsource 93 into detector 40. For example, in certain embodiments, lightfrom calibration source 93 can be coupled into detector 40 using one ormore mirrors and/or beamsplitting elements and/or partial reflectors.These elements can be position either internal or external to a housingenclosing detector 40. Light from calibration source 93 can thereby bedirected along either a portion of the same optical path traveled byemitted light 14, or along a different optical path, e.g., along aparallel optical path, to reach the active element(s) of detector 40.

In some embodiments, system 10 can include an automated samplepositioning system 100. Positioning system 100 can be implemented in avariety of ways. In certain embodiments, for example, positioning system100 includes a three-axis stage, with actuators configured to translatesample 20 independently along each of three orthogonal coordinate axes.Processor 50 delivers control signals to positioning system 100 throughcontrol line 102 that cause system 100 to translate sample 20.Measurement of average maximum signal intensity, described above, can beused as a feedback mechanism to determine when sample 20 has beenproperly positioned. Sensors 89 and 99 provide feedback signals toprocessor 50 to ensure that sample 20, during the positioning process,does not contact housing 80 or fiber optic collector 90.

In certain embodiments, positioning system 100 can be implemented as amovable conveyor system for translating samples rapidly into positionwith respect to system 10. In such a configuration, which isparticularly amenable to similarly-sized and -shaped samples (e.g., whensystem 10 is used in a manufacturing environment), the variouscomponents of system 10 (e.g., fiber optic collector 90, housing 80)remain relatively fixed in position, while individual samples arepositioned relative to these fixed components. Following minoradjustment of the positions of the components of system 10, the sampleis analyzed and then transported away by positioning system 100, whichthen introduces another sample of similar dimensions.

In some embodiments, during the analysis process, a certain number(e.g., at least four) of pictures of the sample can be taken. Forexample, one can take two pictures of the sample before analysis and twopictures after analysis. In some embodiments, prior to the analysis ofthe sample, one can take a picture of the sample in focus on the samplestage and label it as a “before” picture without a “grid.” Afterselecting an area on the sample to be analyzed and superimposing a gridon the sample but prior to analyzing the sample, one can then take asecond picture of the sample (which is still in focus and sitting on thesample stage) and label it as a “before” picture with a “grid.” Withoutwishing to be bound by theory, it is believed that it can be importantto take pictures prior to sample analysis as a sample may have verysmall variations (e.g., inclusions or surface irregularities) that maybe completely converted into a plasma upon irradiation with a laser. Inthat case, the variations may produce anomalies in the emission spectraof the sample, which can be explained by comparing the pictures takenbefore and after sample analysis.

In certain embodiments, system 10 can be configured to automaticallytake and save the pictures described above. Automatic capture andstorage of such pictures relieves the system operator from the burden ofperforming this task manually, and ensures that pictures are stored inthe same location with systematic naming conventions, and excludes thepossibility that the system operator will forget to capture or store theimages. In some embodiments, detector 40 can be used to capture theimages described above. Alternatively, in certain embodiments, system 10can include another camera that can be used to automatically capture theimages.

In some embodiments, based on the geological material being analyzed,one can select the spacing between irradiation or incident locations ona sample. The irradiation of a sample by a laser that produces a plasmais also known as a “shot.” In some embodiments, the spacing between shotlocations can be at least 10 μm. However, this spacing can increase(e.g., to at least 15 μm or at least 20 μm), for example, based on how asample responds to the conversion from a solid to a plasma. For example,when the sample is gold, the spacing between shot locations is oftenmore than 250 μm as the gold can be completely ablated for a diameter ofabout 200 μm. In certain embodiments, the spacing can be decreased to atleast 100 nm (e.g., at least 1 μm or at least 5 μm).

In certain embodiments, system 10 can be configured to use a variety ofdifferent spot patterns for illuminating sample 20. As described above,commonly used illumination patterns include line patterns and squaregrid patterns. More generally, however, other patterns can also be used.In particular, system 10 can allow a system operator to define specificpatterns of illumination spots for use in illuminating sample 20.

It has been observed that when the shot-to-shot interval betweensuccessive laser shots is not constant, measured emitted radiationincludes signal components that reflect the non-constant intervals. FIG.8 shows a graph of measured signal intensity for an 8×8 square gridpattern of 64 laser shots on the surface of a sample. As shown in FIG.8, the measured signal intensity for the first shot of each row of thegrid is markedly lower than the measured signal intensity for othershots in the grid. The time delay between the last shot in one of thegrid rows and the first shot in the next grid row is longer than thetime delay between successive shots in any of the grid rows due tolonger translational motion of source 30. The effects observed in FIG. 8have been consistently recognized by the inventors for certain classesof samples and for certain shot grid patterns.

To reduce and/or eliminate the effects of non-constant delay timesbetween all shots in an exposure pattern, exposure patterns in which thedelay from shot-to-shot is consistent among all shots in the pattern canbe used. In general, a variety of such exposure patterns can beimplemented. For example, FIG. 9 shows a hexagonal shot pattern, withsteps between successive shots in the pattern indicated by arrows. Thedistance of travel between each shot is the same in the pattern of FIG.9, so that the time delay between each shot is also the same.

FIG. 10 shows another shot pattern with successive shots positionedalong the circumference of a circle. Between shots, displacement occursfrom one side of the circle to the other, passing close to (but notthrough) the circle's center. The arrows in FIG. 10 indicate thedirection of translation after each shot. By moving along a path that isdisplaced slightly from the center of the circle, each shot is displacedalong the circumference of the circle, but the displacement (and timedelay) between successive shots is the same. The density of shots alongthe circumference of the circle can be adjusted by adjusting thedisplacement of the path of translation from the center of the circle.

More generally, a variety of different shot patterns can be used. Asdisclosed above, shot patterns in which the delay time betweensuccessive shots is the same among all shots can be used to reduce oreliminate the measured effects shown in FIG. 8. However, shot patternswith non-constant delay times between certain successive shots,including user defined shot patterns with this characteristic, can alsobe implemented.

In some embodiments, prior to irradiating laser pulses to a sample,other analytical parameters (e.g., laser wavelength, laser power, orspectral delay) in a LIBS system can be set. Based on the geologicalmaterial analyzed, these parameters can be varied slightly to capturethe petrogenetic signature of the sample. In some embodiments, prior toselection of analytical parameters, an analytical parameterdetermination test can be performed. This test can include analyzingmultiple samples of the same mineral from different locations. Thevariable analytical parameters can be changed (e.g., one at a time) toevaluate a large number (e.g., as many as 300) of different permutationsbefore a final set of analytical parameters is established for themineral.

As an example, when the mineral beryl is analyzed, one can use a LIBSsystem having a 266 nm laser, a laser power of 90% (which corresponds tobetween 12 and 20 mJ), a spectral delay of 1.5 μs, and a gain of 150 μs.All of the analytical parameters used during the analysis process can bedocumented, including the size of the superimposed grid (e.g., 2excitation locations by 5 excitation locations) and the location of theinitial shot. Based on the movement allowed by the LIBS system used inthis embodiment, all analyses can be collected at irradiation locationsfrom left to right and then from top to bottom. The collection of theirradiation locations can be important as it helps to determine thenumber of constituent signals in a sample. In some embodiments, thedepth of analysis can vary greatly based on the laser power used.However, the depth of analysis can stay relatively constant within a setof selected analytical parameters.

In general, once all of the analytical parameters of a LIBS system areset, the methods disclosed herein can be performed to analyze areference or unknown sample.

In some embodiments, multiple detectors can be used to measure emittedlight from sample 20 that corresponds to different combinations ofexposure and detector parameters. Parameters that are typically variedduring measurement of sample data include shot-to-shot delay times,laser power, detector gain settings, and the spacing between laser shotson the sample surface. Among these, shot-to-shot delay times and thepower delivered in each shot are particularly amenable to variation and,when varied, provide valuable information that can be used tocharacterize samples.

To measure emitted radiation from sample 20 that corresponds todifferent combinations of exposure and detection parameters, system 10can include multiple detectors. For example, detector 40 can includemultiple detection elements (e.g., multiple spectrometers), each ofwhich is dedicated to measuring emitted radiation that corresponds to adifferent combination of parameters. As an example, a first spectrometercan be configured to detect emitted radiation corresponding to anexposure pattern with a first shot-to-shot delay time, and a secondspectrometer can be configured to detect emitted radiation correspondingto an exposure pattern with a second shot-to-shot delay time.

Multiple detectors can be implemented in system 10 in a variety ofconfigurations. In some embodiments, for example, detector 40 caninclude a housing that encloses multiple detection elements, such asmultiple spectrometers as discussed above. Within the housing, opticalelements such as beamsplitters are positioned to direct portions of theemitted radiation from sample 20 to each of the detection elements. Theoptical elements can be fixed in position, so that each detectionelement receives a portion of the emitted radiation. Alternatively, theoptical elements can be movable so that the emitted radiation isselectively directed to one of the detection elements.

Alternatively, in some embodiments, system 10 can include multipleself-contained detectors. The detectors can be self-containedspectrometers, for example. Light emitted from sample 20 can becollected by fiber optic collector 90, which can include multipledistribution ports, each connected to one of the self-containeddetectors. In such a configuration, each of the detectors receives aportion of the emitted radiation through a corresponding fiberdistribution port. The distribution ports are spliced to the fiber opticcollector 90. As a further alternative, the output end of fiber opticcollector 90 can be positioned in proximity to one or more opticalelements that direct portions of the emitted radiation to the detectors.The optical elements can be fixed in position, so that each detectorreceives a portion of the emitted radiation, or the optical elements canbe movable so that the emitted radiation is selectively directed to oneof the detectors, as described above.

In some embodiments, system 10 includes a gas delivery system. Referringagain to FIG. 6, system 10 can include a gas source 104 connected to adelivery tube 106 with a nozzle. Gas source 104, which can include oneor more actuators, is connected to processor 50 via control line 107.Gas source 104 delivers one or more gases (e.g., argon) to a location inthe vicinity of the spot where radiation 12 is incident on the surfaceof sample 20. Gas delivery tube 106 can be formed from a variety ofmaterials including, for example, plastics.

In certain embodiments, processor 50 can be configured to optimize theposition of gas delivery tube 106. For example, by calculating averagemaximum signal intensity for a shot pattern on the surface of a sampleas described above, processor 50 can determine whether the position ofgas delivery tube 106 should be checked. Alternatively, or in addition,the position of gas delivery tube 106 can checked as part of a normalcalibration routine for system 10.

To check and/or optimize the position of gas delivery tube 106,processor 50 can follow a procedure similar to the one described abovefor iris 60. In general, the actuators in gas source 104 are configuredto position source 104 and delivery tube 106 in three dimensional space.Accordingly, processor 50 iteratively re-positions source 104 anddelivery tube 106 in three dimensions until an optimum position (e.g., aposition that satisfies a threshold condition and/or produces thehighest measured signal) is found. Typically, the position of gasdelivery tube 106 is checked and/or adjusted only after adjustment ofthe positions of the various optical components in system 10.

In some embodiments, system 10 can include one or more environmentalsensors for measuring a variety of conditions relevant to themeasurement results. Referring to FIG. 6, system 10 includes a sensor110 that is configured to measure one or more of temperature, barometricpressure, and humidity. In certain embodiments, system 10 includesseparate sensors for measuring some or all of these quantities.

In certain embodiments, system 10 can include one or more vibrationsensors. In general, low frequency mechanical vibrations can produceartifacts in measurement results that are actively being accumulated,and can also displace various components of system 10 from their properposition, thereby affecting future measurement results. Accordingly,system 10 can include a vibration sensor 120 that detects and reportsvibrations to processor 50. In response to the detection of vibrations,processor 50 can initiate a number of actions. For example, processor 50can initiate a calibration routine to ensure that the various componentsof system 10 remain properly aligned. The calibration routine caninclude any of the alignment steps disclosed herein. As another example,processor 50 can deliver a warning (e.g., a visual and/or audio message)to a system operator that data being currently acquired may becompromised by the detected vibrations.

In some embodiments, system 10 can include a detector (e.g., detector130 in FIG. 6) to measure the optical power of radiation 12 generated bysource 30. Detector 130 can be connected to, or contained within, source30, as shown in FIG. 6, or implemented as a separate device withinsystem 10. In certain embodiments, for example, a beamsplitter or fibersplice within source 30 delivers a small fraction of radiation 12 todetector 130, which measures the power of the fraction of radiation.Processor 50 receives this measurement from detector 130, and usescalibration information to determine the optical power of each lasershot produced by source 30.

Processor 50 can take a variety of actions if the optical power of oneor more shots is too large or too small (e.g., varies by more than 20%from a nominal optical power setting). In some embodiments, processor 50can deliver a warning message to a system operator that the output powerof source 30 is fluctuating beyond tolerance. The message can include asuggestion that source 30 may require service. In certain embodiments,processor 50 can flag measurement results that correspond to shots forwhich the optical power is beyond tolerance levels. Processor 50 canalso be configured to discard such measurements entirely as unreliable.

System 10 can generally be configured to save all system measurementsautomatically and/or continuously. System measurements can be recordedaccording to sample, lot, shift, operator identity, time, and/or testperformed, and can be directly linked or referenced to data filescorresponding to particular samples. Measurements that can be recordedinclude, but are not limited to, detected vibrations, optical shotpower, temperature, humidity, and pressure. In addition, samplemeasurements that are performed when a cleaning shot of radiation isdelivered from source 30 to sample 20 can also be recorded. In someembodiments, these measurements can be saved together with themeasurement data from one or more samples in a standard data structure(e.g., an XML structured file) for later analysis. The data structurecan be extensible in scope to allow additional data types andsub-structures to be included. The data structure can then be processedby other software applications to extract useful information, includingany of the different types of information disclosed herein.

In some embodiments, system 10 can include a configurable user interfacethat permits a system operator to selectively extract any of theinformation stored in the data structure described above. For example,the user interface allows the operator to extract data measured atcertain times, under certain conditions, and/or for certain samples. Theuser interface also allows the operator to perform operations such asaveraging certain portions of the data, integrating certain portions ofthe data, and discarding certain portions of the data, according touser-defined criteria.

As part of the calibration routines described herein, in someembodiments, processor 50 can be configured to perform a spectralcalibration of detector 40. Processor 50 can determine that spectralcalibration is advisable after a certain elapsed period of time, after acertain duration of usage of system 10, after a certain number ofsamples have been analyzed by system 10, and/or following detection ofone or more triggering events such as a collision detection signal fromsensor 89 and/or 99, and/or a measurement of average maximum signalintensity that falls below a threshold value, as described above.

Spectral calibration can be performed in a variety of ways. For anoperator-assisted calibration, processor 50 can deliver a message to thesystem operator that spectral calibration should be performed, and canprovide instructions to the operator to guide him or her through thecalibration process. In some embodiments, the calibration process can beautomated. For example, referring to FIG. 7, system 10 can initiatespectral calibration by de-activating source 30 (so that no emittedradiation 14 enters measurement port 92), and activating calibrationsource 93, so that light from calibration source 93 is coupled intodetector 40 through calibration port 94. Processor 50 can then adjustthe detection wavelength settings of detector 40 to match the knownemission wavelength(s) of calibration source 93. The calibrationinformation from manual or automated calibration can automatically bestored by system 10 and linked or referenced to subsequent measurementdata for samples. System 10 can also permit parameters such as thefrequency with which calibration routines are executed to be adjusted bythe system operator.

In some embodiments, system 10 includes a housing 200 that encloses thesystem components. In general, housing 200 can be formed from a varietyof materials, such as various metals and plastics. Typically, housing200 is formed from an opaque material that blocks scattered radiationfrom passing through housing 200. In certain embodiments, housing 200includes an optically transparent (or partially transparent) window 202that allows a system operator to view the components of system 10 fromoutside housing 200. In some embodiments, housing 200 also includes anaccess panel 204 that can be readily removed to allow a system operatoror service technician to access the components of system 10 withoutfully disassembling housing 200.

The contents of all publications cited herein (e.g., patents, patentapplication publications, and articles) are hereby incorporated byreference in their entirety.

EXAMPLES

The following examples are illustrative and not intended to be limiting.

Example 1

Two hundred and seventy beryl (Be₃Al₂Si₆O₁₈) crystals (var. emeralds)from 9 different locations in 8 different countries were analyzed usingLaser Induced Breakdown Spectroscopy (LIBS). Thirty individual crystalsfrom each location were studied. The countries where beryl samples wereobtained are Afghanistan (AFG), Brazil (BRA), Colombia (COL), Mozambique(MOZ), Pakistan (PAK), South Africa (ZAF), Zambia (ZMB) and Zimbabwe(ZWE). Two separate and unique deposits in Colombia were analyzed inthis study.

A Photon Machines Insight LIBS system was used for this study. Emissionspectra from 30 laser excitations (shots) were collected at 30 uniqueexcitation locations on the surface of each sample. For each excitationlocation analyzed, a single cleaning shot (an excitation on the samplesurface without the emission generated from the excitation beingcollected) was performed prior to the collection shot (an excitation onthe sample surface from which the emission generated from the excitationis collected). Sample ablation for the experiment reported here wasachieved using a Nd:YAG laser operating at 266 nm with a repetition rateof 1 Hz, a typical pulse energy of 13 mJ and a pulse width of about 6ns. The laser beam was focused onto the surface of the sample. A flow of99.9% pure argon covered the surface of the sample to reducecontamination from ambient air. A second lens was used to collect theemission from the laser induced plasma via a fiber optic cable coupledto an Echelle spectrometer with a spectral resolution of 0.02 nm and aspectral range of 200.02-1000.02 nm (40,000 channels). At a delay of 1μs after the laser pulse, the dispersed emission was recorded for aduration of 10 μs by an Intensified Charge-Coupled Device (ICCD) at again setting of 150 μs. The emission spectrum for each shot was savedindependently, without averaging, to an electronic processer using theChromium software that came as a part of the Photon Machines InsightLIBS system.

After data were collected on all 270 beryl samples using theexperimental setup mentioned above, a blind test was performed. The datawere analyzed using both an inventive method described herein (“the M2Smethod”) and a conventional partial least squares discriminant analysis(PLSDA). The PLSDA analysis was performed by a private third party(PTP). The PTP was not instructed to use PLSDA but to use any of thetraditional analysis techniques typically used in the evaluation of LIBSdata. The PTP is well respected and known to those in the art. The PTPwas selected because they had worked previously on geological materialdetermination problems containing far less robust data sets and they areconsidered experts in the field of “complex” LIBS data analysis.

Both the PTP and the M2S group were provided identical raw spectral datafrom the LIBS analysis. The PTP used only “every 5^(th) wavelength” inthe PLSDA (disregarding 80% of the data available for analysis.) Asimple 50/50 split of the data into separate sets for training andevaluation was used by the PTP. The PTP created a reference libraryusing data from 15 samples from each site. The blind samples (containingdata from the 15 remaining samples) were tested and matched to thesamples in the reference library. It was determined by the PTP thatperformance of the predictive model generated using these data peakedaround 20 latent variables.

The group using the M2S method processed all of the data provided in themethod described earlier. Specifically, the raw spectral data for eachsample (comprising 40,000 channels), be it a reference sample or anunknown sample, was converted into sequences of scaled spectra using themethod described earlier in this application. The reference libraryconsisted of the sequences of the scaled spectra of samples from knownorigins.

Each sequence for an unknown sample was compared to every known sequencein the reference library using a 270-fold leave one sample (sequence)out test design. A leave one out methodology begins by removing thefirst sample's sequences from the reference library and recreating thereference library from the sequences of the remaining 269 samples. Thisprocess was repeated for every sample in the test.

The comparison was made based on a weighted K-nearest neighbor algorithmwhich produced a table containing the distance between the unknownsamples sequences and every known sequence in the reference library. Thetable related the distance between the unknown sample and all of theknown samples based on the distance between their respective sequencesof scaled spectra. The table was ordered from the smallest distance tothe largest distance from the unknown sample's sequences to the knownsamples' sequences. Subsequently, each distance was used to compute ascore, which was based on the relationship of the distance between theunknown and known sequences of scaled spectra. The smaller the distancebetween the unknown and known sequences of scaled spectra, the greaterthe value of the score. This is known as the weighting function. Asecond table was created that related the score that each known samplereceived. This table was ordered from the largest score to the smallestscore. The known sample with the highest score was identified as theclosest match to the unknown sample. Thus, the origin of the knownsample identified as having the closest match was assigned to theunknown sample.

The accuracy of the matching in the M2S and PTP groups is summarized inTable 1 below.

TABLE 1 Country M2S Method PLSDA AFG 98.0% 80.0% BRA 98.0% 58.0% COL100.0%  97.3% MOZ 98.0% 88.7% PAK 98.0% 48.7% ZAF 99.0% 96.0% ZMB 95.0%98.7% ZWE 97.0% 44.0% Average   98%   76%

As shown in Table 1, the M2S method provided an average of 98% accuracyin determining country of origin, while PLSDA provided an averageaccuracy of only 76%.

After the conclusion of the study, the PTP was asked why they haddisregarded 80% of the data. The PTP indicated that an excessive amountof data had been provided and that using any conventional data analysistechniques with that much data would require too much processing time.

Example 2

90 coupons of 17-4 Stainless Steel, all initially originating from thesame bar, but with three different conditions (heat treatments), wereanalyzed using Laser Induced Breakdown Spectroscopy (LIBS). Thirtyindividual coupons from each treatment were studied. The treatmentsstudied were Condition A; Condition H900, and Condition H1150. Thematerial analyzed in this study did not come with certificates, thus thespecifics of the heat treatment can not be certified. In general thefollowing is true for each of the conditions studied.

Condition A is the original annealed condition for the bar, no heattreatment or aging. Condition H900 was age hardened at 482° C. for 1hour, and then air cooled. Condition H1150 was heated at 760° C. for 2hours and air cooled, then heated at 621° C. for 4 hours, and then aircooled.

A Photon Machines Insight LIBS system was used for this study. Emissionspectra from 64 laser excitations (shots) were collected at 64 uniqueexcitation locations on the surface of each sample. For each excitationlocation analyzed, a single cleaning shot (an excitation on the samplesurface without the emission generated from the excitation beingcollected) was performed prior to the collection shot (an excitation onthe sample surface from which the emission generated from the excitationis collected). Sample ablation for the experiment reported here wasachieved using a Nd:YAG laser operating at 1064 nm with a repetitionrate of 1 Hz, a typical pulse energy of 90 mJ and a pulse width of 6 ns.The laser beam was focused onto the surface of the sample. A flow of99.9% pure argon covered the surface of the sample to reducecontamination from ambient air. A second lens was used to collect theemission from the laser induced plasma via a fiber optic cable coupledto an Echelle spectrometer with a spectral resolution of 0.02 nm and aspectral range of 200.02-1000.02 nm (40,000 channels). At a delay of1.25 μs after the laser pulse, the dispersed emission was recorded for aduration of 10 μs by an Intensified Charge-Coupled Device (ICCD) at again setting of 200 μs. The emission spectrum for each shot was savedindependently, without averaging, to an electronic processer using theChromium software that came as a part of the Photon Machines InsightLIBS system.

Using the experimental setup described above, each sample was analyzed64 times; for a total of 1920 collection shots for each condition. Atotal of 5760 collection shots across all three conditions werecollected. The raw spectral data for each sample (comprising 40,000channels), was converted into sequences of scaled spectra using themethod described earlier in this application. The reference libraryconsisted of the sequences of the scaled spectra of samples.

Each sequence for an unknown sample was compared to every known sequencein the reference library using a 90-fold leave one sample (sequence) outtest design. A leave one out methodology begins by removing the firstsample's sequences from the reference library and recreating thereference library from the sequences of the remaining 89 samples. Thisprocess was repeated for every sample in the test.

The comparison was made based on a weighted K-nearest neighbor algorithmwhich produced a table containing the distance between the unknownsamples sequences and every known sequence in the reference library. Thetable related the distance between the unknown sample and all of theknown samples based on the distance between their respective sequencesof scaled spectra. The table was ordered from the smallest distance tothe largest distance from the unknown sample's sequences to the knownsamples' sequences. Subsequently, each distance was used to compute ascore, which was based on the relationship of the distance between theunknown and known sequences of scaled spectra. The smaller the distancebetween the unknown and known sequences of scaled spectra, the greaterthe value of the score. This is known as the weighting function. Asecond table was created that related the score that each known samplereceived. This table was ordered from the largest score to the smallestscore. The known sample with the highest score was identified as theclosest match to the unknown sample. Thus, the Condition of the knownsample identified as having the closest match was assigned to theunknown sample.

TABLE 2 Condition of 17-4 Stainless Steel M2S Method Condition A 96.7%Condition H900 98.9% Condition H1150 97.8% Average 97.8%

As shown in Table 2, the M2S method provided an average of 97.8%accuracy in determining the condition of 17-4 stainless steel.

As discussed earlier, peaks in the data were initially thought to merelyrepresent atomic emissions. However, it has now been shown that peaksnot only contain atomic emissions, but also represent isotopic,molecular, and molecular isotopic emissions. Some data in the peaks maybe produced by spectral interference. For example the concentrations ofMg and Na may not be accurate quantitative values. Rather, due to theinteraction in the plasma, some concentrations of one element, say Na,appear to be greater than what is actually present, and Mg appears tohave a lower concentration than what is actually present, due to thespectral interference phenomenon. Moreover, the shape of the peaks iscritical, because the shape may represent re-absorption of the emittedelement. This is ordinarily seen as a flat top of a peak rather than apoint.

FIG. 5 displays three graphs (Q-set). Each graph is a visualrepresentation of the different members of the sequence that were foundin a sample of the Condition H900 17-4 Stainless Steel. A magnified viewof approximately 1600 channels of the spectrum of the sequence member isprovided to the right in a circle. Each area magnified is the samesection of the sequence. The top graph represents the Prime-Q (2Qn4),this is the primary constituent found in all of the data for the 17-4Stainless Steel. The second graph (2Qn2) and the third graph (2Qn7)represent two other members of the sequence for the Condition H900samples.

Across all three conditions, a total of 9 different members of thesequence were seen. Table 3 shows the frequency of each condition. Onlythree members of the sequence were seen in any one sample at any giventime.

TABLE 3 2Qn4 Condition 2Qn1 2Qn2 2Qn3 (Prime-Q) 2Qn5 2Qn6 2Qn7 2Qn8 2Qn9A 0 11 21 1847 20 14 7 0 0 H900 4 23 13 1847 15 2 9 5 2 H1150 15 15 61848 13 2 6 8 7 Total # of 19 49 40 5542 48 18 22 13 9 Occurrences

In the case of the Condition A samples, the primary constituent (2Qn4)appeared 5542 out of 5760 times. The 2Qn1, 2Qn8, and 2Qn9 constituentsdid not appear at all in the Condition A samples. The 2Qn2 constituentappeared 11 times, the 2Qn3 appeared 21 times, the 2Qn5 constituentappeared 20 times, the 2Qn6 constituent appeared 14 times, and the 2Qn7constituent appeared 7 times.

In the case of the Condition H900 samples and the Condition H1150samples, the sequence of spectra produced the same members: 2Qn1, 2Qn2,2Qn3, 2Qn4, 2Qn5, 2Qn6, 2Qn7, 2Qn8, and 2Qn9. However, the probabilitydistribution for these members differed from that obtained from theCondition A sample. Specifically, the 2Qn1, 2Qn8, and 2Qn9 constituentswere found in the Condition H900 (4 occurrences) and the Condition H1150(15 occurrences) samples but were absent from the Condition A samples.The 2Qn2 constituent appeared 23 out of 5760 times in Condition H900 and15 out of 5760 times in Condition H1150. The 2Qn3 constituent appeared13 out of 5760 times and 2Qn4 appeared 1847 out of 5760 times inCondition H900. In this same condition, the 2Qn5 constituent appeared 15out of 5760 times, 2Qn6 appeared 2 out of 5760 times, 2Qn7 appeared 9out of 5760 times, 2Qn8 appeared 5 out of 5760 times, and 2Qn9 appeared2 out of 5760. In the case of the Condition H1150 samples, the sequenceof spectra produce the same primary constituent, 2Qn4, which appeared1848 out of 5760 times. The 2Qn1 constituent appeared 15 out of 5760times and 2Qn2 appeared 15 out of 5760 times. In this same condition,the 2Qn3 constituent appeared 6 out of 5760 times and 2Qn5 appeared 13out of 5760. The 2Qn6 and 2Qn7 constituents appeared 2 and 6 out of 5760times, respectively, and the 2Qn8 and 2Qn9 constituents appeared 8 and 7out of 5760 times, respectively. By comparing the probabilitydistribution of the member for all three types of samples, the samplescould be distinguished among one another as set forth in Table 2 above.

Referring again to FIG. 5, five unique areas within the magnified areahave been selected and highlighted. These areas are meant to helpvisually display the differences in the data that the algorithmidentifies. One will notice that the location, size, and shapes of thepeaks in each of these three boxes are unique, and the algorithm is ableto identify these differences. In Box One of each of the three graphs,one will notice that the height of the first peak in the bottom graph(2Qn7) is greater than that of the same peak in graphs 2Qn4 and 2Qn2.The second peak in Box One is highest in the first graph (2Qn4) andalmost non-existent in the third (2Qn7). As one continues to visuallyinspect the data, numerous differences can be seen. These differencescharacterize each member of the sequence.

Other Embodiments

A number of embodiments have been described. Nevertheless, it will beunderstood that various modifications may be made without departing fromthe spirit and scope of the disclosure. Accordingly, other embodimentsare within the scope of the following claims.

What is claimed is:
 1. A method for analyzing a sample, the methodcomprising: exposing the sample to plurality of pulses ofelectromagnetic radiation to convert a portion of the sample into aplasma by directing the pulses to be distributed among different spatialregions on a surface of the sample, the plurality of pulses defining anexposure sequence and the spatial regions defining a two-dimensionalexposure pattern on the sample surface; recording a spectrum ofelectromagnetic radiation emitted in response to each of the pluralityof pulses to define a sequence of spectra for the sample, wherein eachmember of the sequence corresponds to the spectrum recorded in responseto a different one of the pulses; using an electronic processor tocompare the sequence of spectra for the sample to a sequence of spectrafor each of multiple reference samples in a reference library; and usingthe electronic processor to determine information about the sample basedon the comparison to the multiple reference samples in the library,wherein exposing the sample to the plurality of pulses ofelectromagnetic radiation comprises directing single pulses from amongthe plurality of pulses to be incident on each of the spatial regions ofthe exposure pattern in sequence, and directing additional single pulsesfrom among the plurality of pulses to be incident on at least some ofthe spatial regions of the exposure pattern, so that multiple pulses areincident on the at least some of the spatial regions; wherein for eachone of the at least some of the spatial regions, a total of two or morepulses are incident at a common location on the sample that correspondsto the one of the at least some of the spatial regions; and wherein forthe exposure sequence, a temporal delay between each one of theplurality of pulses and an immediately prior pulse in the exposuresequence is a constant value D.
 2. The method of claim 1, wherein thespatial regions form a hexagonal array on the sample.
 3. The method ofclaim 1, wherein the spatial regions form an array of equally spacedexposure regions on the sample.
 4. The method of claim 3, wherein theexposure regions are positioned along a circumference of a commoncircle.
 5. The method of claim 1, wherein the plurality of pulsescomprises 60 or more pulses.
 6. The method of claim 1, furthercomprising adjusting a spacing between the different spatial regions ofthe sample and maintaining the temporal delay at the constant value D.7. The method of claim 6, further comprising selecting the temporaldelay based on the sample.
 8. The method of claim 6, further comprisingselecting a power of each of the plurality of radiation pulses.
 9. Themethod of claim 6, further comprising measuring a first sequence ofspectra for the sample corresponding to a first constant temporal delay,and measuring a second sequence of spectra for the sample correspondingto a second constant temporal delay that is different from the firstconstant temporal delay.
 10. The method of claim 9, further comprisingdetermining the information about the sample based on the first andsecond sequences of spectra.
 11. The method of claim 1, wherein eachmember of the sequence of spectra corresponds to a different one of thespatial regions and comprises information from a single measurement of aspectrum of electromagnetic radiation emitted from the different one ofthe spatial regions.
 12. A system for analyzing a sample, the systemcomprising: an electromagnetic radiation source configured to expose thesample to plurality of pulses of electromagnetic radiation to convert aportion of the sample into a plasma; a detector configured to record aspectrum of electromagnetic radiation emitted in response to each of theplurality of pulses to define a sequence of spectra for the sample,wherein each member of the sequence corresponds to the spectrum recordedin response to a different one of the pulses; and an electronicprocessor configured to: control the radiation source to direct theplurality of electromagnetic radiation pulses to be distributed amongdifferent spatial regions on a surface of the sample such that: theplurality of pulses define an exposure sequence and the spatial regionsdefine a two-dimensional exposure pattern on the sample surface; singlepulses from among the plurality of pulses are incident on each of thespatial regions of the exposure pattern, and additional single pulsesfrom among the plurality of pulses are incident on at least some of thespatial regions of the exposure pattern, so that multiple pulses areincident on the at least some of the spatial regions; for each one ofthe at least some of the spatial regions, a total of two or more pulsesare incident at a common location on the sample that corresponds to theone of the at least some of the spatial regions; and for the exposuresequence, a temporal delay between each one of the plurality of pulsesand an immediately prior pulse in the exposure sequence is a constantvalue D; compare the sequence of spectra for the sample to a sequence ofspectra for each of multiple reference samples in a reference library;and determine information about the sample based on the comparison tothe multiple reference samples in the library.
 13. The system of claim12, wherein electronic processor is configured to control the radiationsource so that the spatial regions form an array of equally spacedexposure regions on the sample.
 14. The system of claim 12, wherein theelectronic processor is configured to control the radiation source sothat the spatial regions form a hexagonal array on the sample.
 15. Thesystem of claim 12, wherein electronic processor is configured tocontrol the radiation source so that the spatial regions form an arrayof equally spaced exposure regions on the sample.
 16. The system ofclaim 15, wherein the exposure regions are equally spaced along acircumference of a circle.
 17. The system of claim 12, wherein theplurality of pulses comprises 60 or more pulses.
 18. The system of claim12, wherein the electronic processor is configured to adjust a spacingbetween the different spatial regions of the sample, and to maintain thetemporal delay at the constant value D.
 19. The system of claim 18,wherein the electronic processor is configured to select the temporaldelay based on the sample.
 20. The system of claim 18, wherein theelectronic processor is configured to control the radiation source toselect a power of each of the plurality of radiation pulses.
 21. Thesystem of claim 18, wherein the electronic processor is configured to:use the detector to measure a first sequence of spectra for the samplecorresponding to a first constant temporal delay; and use the detectorto measure a second sequence of spectra for the sample corresponding toa second constant temporal delay that is different from the firstconstant temporal delay.
 22. The system of claim 21, wherein theelectronic processor is configured to determine the information aboutthe sample based on the first and second sequences of spectra.